Benchmarking AI Agents for Addressing Scientific Challenges Across Scales

2026-06-10

AI Summary

Current AI agents lack systematic evaluation in authentic scientific research settings, as prevailing benchmarks fail to capture the complexity, heterogeneity, and long-horizon reasoning inherent in real-world scientific tasks. To address this gap, this work proposes SciAgentArena—the first systematic benchmark enabling interactive, open-ended evaluation of scientific agents. It comprises approximately 200 step-validated, multi-domain real-world research tasks within an agent-agnostic interactive environment. Empirical evaluation using this framework reveals that while existing agents can handle structured data analysis, they exhibit significant limitations in autonomous exploration, innovative insight generation, and solving open-ended scientific problems. The study further identifies recurring failure patterns, offering clear directions for future improvements in scientific AI agent design.

Abstract

AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchmarks for AI agents rarely capture the complexity, heterogeneity, and extended reasoning required by scientific work, whereas benchmarks for scientific tasks often reduce research to static, direct problems and provide limited support for interactive evaluation. Here, we introduce SciAgentArena, a systematic benchmark for evaluating AI agents in real-world scientific research scenarios drawn from emerging needs across multiple domains. SciAgentArena comprises approximately 200 tasks with stepwise verification and an interactive, agent-agnostic environment for assessing diverse AI agents. Using this benchmark, we find that current agents can contribute effectively to well-specified data-analysis workflows, particularly when the task structure and evaluation criteria are clear. However, their performance remains uneven across scientific contexts: agents struggle to generate genuinely novel insights, sustain self-directed exploration, and formulate robust solutions for open-ended research questions. We further characterize common failure modes across agents and identify opportunities for improving their reliability, autonomy, and scientific reasoning. Together, SciAgentArena provides a practical framework for measuring progress in AI agents for science and for guiding the design of future agents capable of addressing complex scientific challenges. Full codes, tasks, and datasets can be accessed via this link: https://sciagentarena.github.io/.

Problem

• AI agents
• scientific discovery
• benchmarking
• interactive evaluation
• open-ended research

Innovation

• AI agents
• scientific benchmarking
• interactive evaluation
• stepwise verification
• open-ended research